3.2 Introduction to Mainstream AI Image Generation Tools

Creating Beyond the Canvas: An Overview of Mainstream AI Image Generation Tools

In recent years, artificial intelligence has experienced explosive growth in its creative capacity within the visual arts domain. Among the most striking developments is the maturation and popularization of “Text-to-Image” technology. These AI image generation tools, akin to possessing a magical brush, can transform a user’s natural language description (Text Prompt) into entirely new, detailed, stylistically diverse, and sometimes breathtaking visual images within a short time.

The magic of this technology is not only causing disruptive waves in creative fields like art creation, advertising design, game development, and film/TV production but is also beginning to knock on the door of the legal industry. It offers unprecedented imaginative possibilities for scenarios such as visualizing case simulations, illustrating complex legal concepts graphically, and creating engaging legal education materials.

However, accompanying this powerful creativity are equally profound and urgent legal and ethical challenges. Core issues include copyright ownership of generated content, copyright disputes arising from training data, the potential risks of deepfakes, and potential biases and discrimination embedded in algorithms.

For legal professionals who must constantly maintain rigor, prudence, and foresight, understanding the features, core technologies, usage methods, capability boundaries, and potential risks of mainstream AI image generation tools is fundamental. It enables effective evaluation, wise selection, responsible application, and anticipation of related legal issues. This section will scan and analyze several prominent mainstream image generation tools.

1. Midjourney: The Community-Driven Platform Pursuing Artistic Excellence

Midjourney has rapidly captured a large user base due to the unique artistic quality, strong atmospheric rendering capabilities, and generally high aesthetic standard of its generated images. It particularly excels in fields like concept art design, illustration creation, and visualizing fantasy/sci-fi styles, regarded by many creative professionals and enthusiasts as a top choice.

• Developer/Platform: Developed and operated by the independent research lab Midjourney, Inc.

• Access and Interaction: Its most distinctive (and formerly sole) interaction method is through the Discord chat platform. Users join Midjourney’s official Discord server and use specific commands (primarily /imagine) followed by an English text prompt in designated channels (“newbie” or member channels) to trigger image generation. The output is typically four candidate images, from which users can choose to Upscale or create Variations. This chatbot-based interaction might feel natural to Discord users but can involve a learning curve for newcomers. More recently, Midjourney has started rolling out a web-based interface for image generation and management to improve user experience.

• Core Technical Basis: Midjourney has not publicly disclosed its detailed technical architecture or model specifics. However, based on its output quality and industry consensus, its core engine is widely believed to be based on state-of-the-art Diffusion Models. It is meticulously trained, fine-tuned, and aesthetically optimized using massive amounts of high-quality artistic image data (potentially including works by many artists) and continuous user preference feedback (user selections, ratings, etc.). This process shapes its unique and widely acclaimed “Midjourney style.”

• Features and Core Strengths:

  • Top-tier Artistic Output Quality: Images generated by Midjourney usually exhibit outstanding aesthetic quality, featuring rich details, captivating lighting and shadow effects, bold yet harmonious color palettes, and often cinematic or painterly compositions. Its “default” style inherently possesses strong artistic flair.
  • Imaginative and Atmospheric: Particularly adept at handling prompts that are imaginative, conceptual, or require creating a specific mood, capable of generating visually engaging styles like surrealism, fantasy, and cyberpunk.
  • Active Community Ecosystem: Its Discord server serves not just as the tool’s entry point but also as an extremely large and active user community. Users showcase work, share successful prompts and parameters, learn techniques from each other, and participate in official events, fostering a unique community-driven atmosphere for inspiration and learning.
  • Rapid Model Iteration: Midjourney releases new model versions (V1 through V6 and potentially beyond) frequently. Each generation typically brings significant improvements in image quality, detail handling, prompt understanding, and stylistic diversity.

• Limitations and Potential Issues:

  • Relatively Weaker Controllability: Compared to Stable Diffusion, which offers more low-level control options, Midjourney users have limited fine-grained control over the generation process. For example, precisely controlling character poses, object layouts, or image structure like with ControlNet is difficult. Midjourney sometimes acts more like an “artist,” potentially interpreting the prompt’s spirit while adding its own “flair,” leading to results that might deviate from the user’s literal interpretation, prioritizing overall visual impact and artistic mood.
  • Closed Source and Fully Paid: Midjourney’s model and code are completely closed source. Users cannot access the underlying model or perform custom modifications. Furthermore, it is a purely paid service. Free trials are often brief, unstable, or non-existent, requiring users to subscribe monthly or annually for continued use.
  • Reliance on Discord Platform: Although a web version is emerging, its core interaction and community remain heavily reliant on Discord, posing a barrier to entry for users unfamiliar or uncomfortable with the platform.
  • Copyright and Training Data Controversies: Like other mainstream image generation models, Midjourney faces disputes regarding the legality of its training data sources (whether unauthorized artist works were used) and the copyright ownership of user-generated images. Its terms of service detail commercial use rights for user-generated images (typically, paying subscribers own the rights to their generations, but Midjourney also retains certain rights). Users, especially commercial ones, need to carefully read and understand these terms.

• Relevance to Legal Scenarios:

  • Visualization Aid (Non-Evidentiary): Due to its strong artistic capabilities, Midjourney might be suitable for generating high-quality, non-evidentiary visual representations of legal concepts (e.g., using artistic images to explain complex legal relationships or principles) or artistic renderings of simulated scenes (e.g., creating visually impactful auxiliary materials for trial presentations or client communication, clearly stating their non-realistic nature).
  • Typical Case for Copyright Issues: Midjourney is often cited when discussing AI-Generated Content (AIGC) copyright issues. Lawyers may need to advise clients on its terms regarding user rights, commercial use, and copyright ownership.
  • Potential Risk Awareness: Users must realize that images generated with Midjourney, even if commercial rights are obtained, might unintentionally contain elements or styles similar to copyrighted works in its training data, posing potential infringement risks (especially when directly mimicking specific artists’ styles). Risk assessment is necessary for commercial applications.

2. Stable Diffusion: The Open-Source Pioneer with Control and a Thriving Ecosystem

Stable Diffusion was developed primarily through the support of the startup Stability AI, in collaboration with researchers from institutions like the CompVis group (Computer Vision & Learning Research Group) at LMU Munich and RunwayML. Its emergence, particularly the open-sourcing of its core model weights, has had a revolutionary impact on the AI image generation field. It is currently the most influential, widely used, highly customizable, and ecologically rich open-source image generation model.

• Developer/Platform: Stability AI and the global open-source community.

• Access and Deployment Methods: Stable Diffusion offers extremely flexible and diverse access methods:

  • Open-Source Model, Local Deployment: Its core model weights are public, allowing users to download them for free and deploy/run the model entirely offline on their local computers (requires a decent dedicated GPU, with VRAM being a key factor). This provides maximum flexibility, the highest potential for customization, and optimal data privacy (as all computation and data remain local).
  • Online Services and Platforms: For users lacking local deployment capabilities or seeking convenience, numerous online generation websites and services based on Stable Diffusion exist. These include official offerings from Stability AI like DreamStudio and Clipdrop, as well as many platforms built by third-party companies or community developers (often providing limited free credits or requiring paid subscriptions).
  • Integration into Existing Tools: Stable Diffusion’s models and technologies (like ControlNet) are widely integrated into various image editing software (e.g., Photoshop plugins), 3D modeling tools, design collaboration platforms, and automation workflows.

• Core Technical Basis: Stable Diffusion’s underlying architecture is Latent Diffusion Models (LDMs). Unlike earlier diffusion models (like DDPM) or some others that operate directly in pixel space, LDMs work as follows:

  1. Use the encoder of a pre-trained Variational Autoencoder (VAE) to compress the input high-resolution image into a much lower-dimensional yet information-dense latent space, obtaining a latent representation.
  2. The crucial diffusion and reverse denoising process occurs entirely within this low-dimensional latent space. This means the core U-Net denoising network also processes latent representations.
  3. After denoising, a “clean” latent representation is obtained, which is then “decompressed” back into the final high-resolution pixel image using the VAE’s decoder. This strategy of performing major computations in the latent space significantly reduces the model’s demand for computational resources (especially GPU memory), making high-resolution image generation feasible on consumer-grade hardware—a key technical factor behind Stable Diffusion’s rapid popularization.

• Features and Core Strengths:

  • Open-Source Spirit and Open Ecosystem: This is Stable Diffusion’s core value and competitive edge. The openness of model weights allows researchers, developers, and enthusiasts worldwide to freely download, use, modify, study, and redistribute the models (subject to specific open-source licenses like the CreativeML Open RAIL-M license). This has fostered an extremely large, active, and creative open-source community.
  • Unparalleled Controllability and Customizability:
    • Model Fine-tuning: Users can use their own datasets (e.g., specific art styles, product photos, images of a particular person) to fine-tune the base model, enabling it to better generate that specific style or subject. For instance, one could train a model specialized in generating a particular type of legal diagram.
    • LoRA (Low-Rank Adaptation): A lightweight, parameter-efficient fine-tuning technique. Users can train small LoRA “model plugins” (typically a few MB to a few hundred MB), load them onto a base model, and precisely generate specific characters (like anime figures), art styles (e.g., ink wash, oil painting), clothing, objects, or concepts without modifying the massive base model. LoRA significantly lowers the barrier to model personalization, with countless community-created LoRAs available for download.
    • ControlNet: An extremely powerful extension framework allowing users to provide an additional “control image” (e.g., line art, human pose skeletons, depth maps, edge maps, semantic segmentation maps) during generation. This enables highly precise control over the final image’s composition, character poses, object shapes, spatial layout, etc. ControlNet elevates text-to-image from random chance to precise, guided generation.
    • Rich Parameters and Workflows: Various Stable Diffusion user interfaces (like Automatic1111 WebUI, ComfyUI) offer extensive, fine-grained adjustable parameters (e.g., choosing different Samplers, adjusting Steps, controlling CFG scale, setting Seeds, selecting VAEs) and support building highly complex and customized generation workflows through node-based programming (e.g., ComfyUI).
  • Flexibility and Privacy Protection: The ability for local deployment allows users full control over their data. This provides a natural privacy advantage for scenarios involving sensitive information (e.g., generating confidential case-related visualizations in legal settings).
  • Community-Driven Innovation: The open-source community constantly produces new techniques, tools, optimization methods, tutorials, and creative applications, keeping the Stable Diffusion ecosystem vibrant and rapidly evolving.

• Limitations and Potential Issues:

  • Relatively High Barrier to Entry: Deploying locally and fully utilizing advanced features (like training LoRAs, using complex ControlNet workflows, fine-tuning parameters) typically requires users to have some technical knowledge (Python, Git, command line) and good hardware (especially GPU VRAM, 8GB+ recommended, more is better). Using online services or simplified interfaces lowers this barrier.
  • “Out-of-the-Box” Results May Require Tuning: Compared to the potentially “one-click masterpiece” results from Midjourney or DALL-E 3, generating images directly with Stable Diffusion’s base models might require users to master more effective prompting techniques (including Negative Prompts to exclude unwanted content), select appropriate samplers and parameters, or even use specific community fine-tuned models (Checkpoints) or LoRAs to achieve optimal, intended results. It can involve a learning curve for beginners.
  • Prominent Copyright and Ethical Risks:
    • Training Data Controversy: Stable Diffusion’s base models were primarily trained on large datasets like LAION-5B, scraped from the internet. These datasets inevitably contain vast amounts of copyrighted images and photos of real people, leading to significant copyright infringement controversies and multiple ongoing lawsuits (e.g., by Getty Images, artist collectives). The potential copyright infringement risk of using Stable Diffusion-generated images, especially commercially, is a critical concern for users.
    • Risk of Misuse: Its open-source and locally deployable nature also means Stable Diffusion is more easily misused for generating inappropriate, harmful, infringing, or illegal content (e.g., deepfakes, misinformation, hate speech, non-consensual pornography). While the models often include safety filters, these can be easily bypassed or removed in local deployments. Therefore, users bear greater legal and ethical responsibility.

• Relevance to Legal Scenarios:

  • Core Focus of Copyright Litigation: Stable Diffusion is central to current AI copyright lawsuits. The outcomes of these cases will profoundly impact the entire AI generation field and warrant close attention and study by the legal community (especially IP lawyers).
  • Privacy Advantage of Local Deployment: For entities handling highly sensitive information like law firms, legal departments, or judicial bodies, who wish to generate images within their internal network (e.g., confidential case visualizations, simulations, training materials), locally deployed Stable Diffusion offers a crucial possibility.
  • Potential for Precise, Controlled Generation: Technologies like ControlNet give Stable Diffusion unique advantages in scenarios requiring image generation based on specific sketches, structures, or poses. For example, a lawyer could provide a simple accident scene sketch for the AI to generate a more detailed but structurally consistent diagram (still emphasizing its non-evidentiary nature).
  • Requires High Risk Awareness: Given its prominent copyright controversies and misuse risks, legal professionals must conduct extremely cautious risk assessments and potentially seek specialized legal advice before using Stable Diffusion, particularly in commercial or public contexts.

3. DALL-E Series (OpenAI): Strong Prompt Adherence and Seamless Ecosystem Integration

DALL-E is a renowned text-to-image model series developed by OpenAI, favored by users for its ability to understand and follow complex, sometimes counter-intuitive text prompts relatively well, and its seamless integration with OpenAI’s powerful ecosystem (especially ChatGPT).

• Core Model Evolution:

  • DALL-E (2021): One of the pioneering works in the field, based on an autoregressive model (similar to how GPT handles text, treating images as sequences of discrete visual tokens). It showcased AI’s stunning ability to generate diverse, creative, and even surreal images from text, but with limited resolution and realism.
  • DALL-E 2 (2022): Marked OpenAI’s shift towards diffusion models. Compared to the original, DALL-E 2 achieved massive leaps in resolution, realism, and detail. It introduced the CLIP (Contrastive Language-Image Pre-training) model as a key bridge between text and image semantics. Its generation process roughly involves encoding the text prompt into a CLIP text embedding, using a prior model to “translate” this into a corresponding CLIP image embedding, and finally using a diffusion-based decoder to generate the final pixel image from this image embedding. DALL-E 2 also introduced important image editing features like Inpainting (filling in parts of an image) and Outpainting (extending image boundaries).
  • DALL-E 3 (2023): The current latest and most powerful version. Its core advancement lies in significantly improved understanding and adherence to user prompts, especially when handling prompts involving multiple objects, complex spatial relationships, specific attributes, and text generation within the image. A key innovation of DALL-E 3 is its typical deep integration with Large Language Models (like GPT-4) (e.g., within ChatGPT Plus/Team/Enterprise and Microsoft Copilot). Users can input relatively simple, natural language descriptions, and ChatGPT leverages its powerful language understanding and reasoning abilities to automatically rewrite or expand the user’s intent into more detailed, richer internal prompts optimized for image generation, which are then passed to the DALL-E 3 engine. This “built-in prompt engineer” mechanism greatly lowers the barrier to entry, making it easier for average users to obtain high-quality, highly relevant results.

• Access Method: The DALL-E series is primarily a closed-source commercial service. Users access it mainly through OpenAI’s API or via integration into its paid products (like ChatGPT Plus/Team/Enterprise). Microsoft also integrates DALL-E 3 functionality into its Copilot and Bing Image Creator products.

• Technical Basis: Both DALL-E 2 and DALL-E 3 are considered to be based on diffusion models. DALL-E 3 particularly emphasizes synergy with LLMs (like GPT-4) to enhance deep understanding and transformation of natural language prompts.

• Features and Core Strengths:

  • Excellent Prompt Understanding and Adherence: DALL-E 3, in particular, better understands complex details, subtle nuances, spatial relationships, and compositional concepts in user prompts, generating images highly consistent with the request.
  • Ability to Generate Images Containing Text: Compared to other contemporary models, DALL-E 3 has made significant strides in generating relatively accurate and legible text within images (though not perfect and still prone to errors).
  • High Ease of Use: Through integration with ChatGPT, users can use very natural, simple language for descriptions without needing complex prompt engineering skills, resulting in a very user-friendly experience.
  • Robust Safety Design and Content Filtering: OpenAI implements significant safety training and content filtering during deployment to proactively reduce the generation of harmful, discriminatory, infringing, or policy-violating content (though this can sometimes overly restrict creative expression).
  • Mature API Support: Provides stable, well-documented APIs, making it convenient for developers to integrate DALL-E’s image generation capabilities into their own applications or services.

• Limitations:

  • Completely Closed Source: The model itself is not open source, preventing local deployment or custom fine-tuning.
  • Typically Requires Payment: Using DALL-E 3 usually requires subscribing to services like ChatGPT Plus or paying based on API call volume.
  • Style Can Be Relatively “Neutral” or “Standardized”: Compared to Midjourney’s strong artistic bias or the infinite stylistic possibilities from the Stable Diffusion community, DALL-E’s generated images are sometimes perceived as having a relatively “neutral” default style or lacking distinct personalized characteristics (though users can guide styles via prompts).
  • Stricter Content Restrictions: Due to safety concerns and brand image, OpenAI imposes relatively strict limitations on the types of content that can be generated (e.g., involving public figures, violent scenes, adult content, politically sensitive topics), sometimes preventing users from generating content they deem reasonable.

• Relevance to Legal Scenarios:

  • Convenience through Ease of Use: For legal professionals with limited technical background who need to quickly generate auxiliary images (e.g., illustrations for internal training, concept diagrams for presentations, simple explanatory figures for informal reports), DALL-E 3 integrated with ChatGPT offers a very low entry barrier and smooth user experience.
  • Potential Value of Text Generation: Its relatively better ability to generate text within images might be advantageous for creating legal diagrams that require a few key labels, titles, or annotations (e.g., flowcharts, organizational charts, simple evidence chain diagrams).
  • Copyright Stance Needs Attention: OpenAI’s terms of service generally state that users own the images they generate via DALL-E services (provided they comply with terms and the content is legal). However, the terms also usually emphasize that users are solely responsible for ensuring their prompts and generated images do not infringe any third-party rights (including copyright, trademark, publicity rights, etc.). Transparency regarding its training data sources is also limited, and potential copyright risks persist.

4. Other Noteworthy Forces in Image Generation

Beyond the “big three,” other significant players and technological directions in the AI image generation field warrant attention:

• Adobe Firefly: This is a suite of generative AI models and features launched by Adobe, deeply integrated into its Creative Cloud software suite (e.g., Photoshop, Illustrator, Adobe Express). Firefly’s main selling point and differentiator is its commitment to copyright safety for commercial use. Adobe claims the data used to train its Firefly image models comes exclusively from Adobe Stock content explicitly licensed for training, open-licensed content, and public domain content where copyright has expired. It also offers IP indemnification for commercially used Firefly-generated content (subject to specific conditions).

  • Legal Relevance: For enterprise users (including law firms and large corporate legal departments) who highly prioritize copyright compliance and want to safely use AI-generated images for commercial purposes (e.g., marketing materials, client reports, website illustrations), Adobe Firefly presents a relatively lower-risk option. Its seamless integration with Adobe design tools also enhances designer productivity.

• Rise of Tools Prominent in China: Tech companies and research institutions in China are also actively developing competitive products and services in the image generation space. Examples include:

  • Baidu’s ERNIE-ViLG (“文心一格”): Leveraging the ERNIE foundation model, it offers text-to-image, image-to-image functions, potentially excelling at understanding Chinese prompts and generating images with Chinese cultural elements.
  • Alibaba’s Tongyi Wanxiang (“通义万相”): Part of the Tongyi large model family, it also provides image generation capabilities, possibly integrating with its e-commerce and design ecosystems.
  • Others: Potentially including relevant products or technologies from companies like Tencent, ByteDance, SenseTime, etc.
  • Legal Relevance: For applications primarily targeting the Chinese market, requiring generation that aligns with Chinese cultural context and aesthetic preferences, or needing to meet specific domestic regulatory requirements, using tools developed in China might be more appropriate.

5. Core Considerations for Selection and Use: A Legal Perspective

Faced with a dazzling array of AI image generation tools, legal professionals should focus on the following factors when selecting and using them, always adhering to professional ethics:

• Clarify Purpose and Context: What is the primary goal of generating the image? Is it for internal learning, informal communication, client reports, non-evidentiary trial exhibits, or public marketing and website content? Different application scenarios have vastly different requirements for image quality, style, controllability, and especially copyright safety.
• Assess Copyright Risk and Compliance:
  • Training Data Sources: Understand the tool’s training data sources and assess potential copyright infringement risks. For commercial use, prioritize tools with explicit copyright safety commitments like Adobe Firefly, or carefully evaluate the risks of open-source models (like Stable Diffusion) and consider licensing/insurance.
  • Terms of Service Details: Carefully read and understand the chosen tool’s terms of service, particularly regarding ownership of generated content, usage rights (personal/commercial), prohibited content, and disclaimers.
  • Generated Content Review: Manually review generated images to determine if they constitute substantial similarity to existing copyrighted works (including trademarks, design patents, etc.) to avoid unintentional infringement.
• Prioritize Factual Accuracy and Avoid Misrepresentation:
  • Remember the Nature of AI Images: They are “creations” or “compilations” based on statistical patterns, not factual records or precise reflections of reality.
  • Never Use as Direct Evidence: AI-generated images cannot be used directly to prove facts in a case; their authenticity cannot be guaranteed.
  • Use Cautiously for Simulation and Visualization: If used for case simulation, accident scene illustration, legal relationship diagrams, or other auxiliary understanding purposes, it must be clearly labeled as “simulation,” “illustration,” or “AI-generated.” Ensure the core factual elements it is based on are accurate (e.g., generate based on an accurate sketch or layout using ControlNet). Strenuously avoid generating content that could distort facts, mislead, or carry undue bias.
• Be Vigilant Against Deepfakes and Impersonation: Recognize that image generation technology (especially combined with facial editing) can be misused to create fake IDs, forge photographic evidence, or generate defamatory or insulting images targeting individuals. Maintain skepticism when encountering suspicious image evidence or online information.
• Identify and Manage Bias Risks: Be aware that AI-generated images can reflect or amplify societal biases or stereotypes present in their training data (e.g., stereotypical depictions of gender or race in certain professions or groups). When using generated images (especially publicly), take care to avoid propagating or reinforcing these biases.
• Protect Data Privacy and Client Confidentiality: When using any AI image generation tool (especially cloud-based services or public online platforms), strictly prohibit inputting any client identities, case details, trade secrets, or any other confidential sensitive information into text prompts. Prioritize solutions that can run locally (like Stable Diffusion local deployment) or offer strong data privacy protection guarantees.

Summary

AI image generation technology opens up a new world of infinite visual creative possibilities. For legal professionals, understanding the characteristics of mainstream tools—Midjourney’s artistic allure, Stable Diffusion’s open-source power and controllable potential, the DALL-E series’ intelligent ease of use, and Adobe Firefly’s commitment to copyright safety—while deeply recognizing the associated core risks like copyright ownership, factual accuracy, deepfakes, and bias propagation, is essential. This knowledge forms the foundation for prudently, responsibly, and creatively utilizing this technology in an era where “seeing is not always believing,” while upholding the professional bottom line and the spirit of the rule of law. The next chapter will explore AI’s capabilities in another crucial sensory dimension—hearing—focusing on speech and audio processing technologies.